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新公共管理理论的出现,为我国的地方政府绩效审计执行工作的进行提出了新的理念.文章从分析我国地方政府绩效审计工作中存在的工作不到住及公开性较差入手,在对公共管理环境下地方政府绩效审计执行特征分析的基础上,提出了加强地方政府绩效审计执行的策略. 相似文献
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本文根据霍夫斯塔德的跨文化比较理论,将我国与英、美等国家在五个文化维度上的差异进行了对比,在此基础上分析了文化差异对我国地方政府绩效评估管理可能产生的影响,并据此对我国实施地方政府绩效管理提出政策建议,以消除或缓解影响,促进我国地方政府绩效管理的健康发展. 相似文献
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标杆管理——推进地方政府绩效评估改进的有效途径 总被引：2，自引：0，他引：2
标杆管理是一种不断认识和引进最佳实践,以提高组织绩效的方法.将标杆管理引入政府绩效评估体系已成为西方国家政府绩效管理改革的一个重要趋势之一.文章在论述标杆管理的理论基础上,分析了标杆管理介入地方政府绩效评估的原因,并从政府绩效评估标准、评估指标、评估方法及评估程序等几个方面论述标杆管理对政府绩效评估系统的改进,论证了标杆管理在我国地方政府绩效评估中的重要地位. 相似文献
4.
政府绩效评估是国家在现存政治制度的基本框架内、在政府部分职能市场化和公共服务输出市场化以后、在政府公共部门与公众关系基本定位的前提下由民权运动和科学技术推动下所采取的政府治理方式。从分析公共管理中绩效评估的内涵及绩效管理的理念入手,提出我国政府绩效评估的研究构架,具有较大的现实意义。 相似文献
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我国地方政府绩效目标设置的研究——基于目标设置理论视角 总被引：1，自引：0，他引：1
绩效目标设置是地方政府绩效管理的起始点,也是关键环节之一。本文针对我国地方政府绩效目标设置中存在问题,根据目标设置理论及其相关研究成果对地方政府绩效目标设置进行了分析,提出了地方政府绩效目标设置模型及其分解模式,并对地方政府绩效目标设置的技术和方法进行了探讨。 相似文献
6.
绩效考核是现代人力资源管理的一个重要环节,它为人力资源管理各个方面提供反馈信急,是组织人员工资管理、晋升、人员使用和培训的主要依据,也是调动员工积极性的重要环节.绩效考核作为人力资源管理与开发的手段、前提和依据,其最终目的是提高成员的绩效以改善组织绩效.要提高我国政府的工作绩效,必然要求政府工作人员即公务员工作绩效的提高.因此,政府的绩效管理成为20世纪末以来各国行政改革的一个重要内容. 相似文献
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在城乡发展一体化背景下,在广大乡村进行所谓的"类"地方自治改革的理由并不充分.对此,本文主要从两个方面进行论述:一则以自治理论为视角,分析了整体政府理论与村民自治制度的内在张力,弱化了该理论适用于我国乡村治理变革的可能性,从而质疑了"类"地方自治在我国乡村治理改革中的可行性;一则以制度实施为视角,分析了乡村治理问题与村民自治制度实施的关系,否定了制度实施中的问题与制度本身之间的因果关联性,论证了村民自治的异化才是乡村治理低绩效的真实原因,从而动摇了"类"地方自治在我国乡村治理改革中的必要性. 相似文献
9.
高管激励机制的设计和选择是一项庞杂的系统工程,需综合考虑企业内外部环境的复杂性和不确定性,也将对公司治理及绩效产生重要影响.在以往理论研究的基础上,从薪酬、股权和晋升三方面考察高管激励对公司治理及绩效的作用机制.研究表明,单方面考虑高管激励机制对公司治理及绩效的影响是远远不够的,必须将薪酬激励、股权激励和晋升激励纳入到一个整体进行分析,使三种激励效应之间相互补充,从而更好地服务于公司治理的战略决策和实施过程. 相似文献
10.
生猪规模养殖粪污治理演化博弈及影响因素分析 总被引：1，自引：0，他引：1
针对生猪规模养殖粪污治理动力不足、长效运行机制缺失等问题,基于有限理性假设,运用演化博弈理论建立养殖户与地方政府间博弈的复制动态方程,得到不同情景下养殖户和地方政府博弈的进化稳定策略及主要影响因素;在模型求解的基础上,利用MATLAB仿真工具分析不同参数变量情景下养殖户与地方政府决策行为的动态调整过程。研究得出:地方政府和养殖户在生猪粪污治理策略执行过程中的动态演化,在很大程度上取决于地方政府的引导力度、引导成本、奖惩力度和养殖户治理力度、成本、收益等因素的大小。基于此,建议地方政府加强引导和宣传培训、建立高效奖惩机制、加快技术创新与服务支持,以期实现生猪规模养殖粪污治理策略优化、提高粪污综合利用率,促进环境绩效向经济绩效的良性转换,实现双重帕累托改进。 相似文献
11.
危机情报可靠性对公共危机伪信息管理绩效影响研究 总被引：1，自引：0，他引：1
首先分析了危机情报与公共危机伪信息管理的密切联系,认为公共危机伪信息管理同时也是一个危机情报活动过程;进而从准确性与全面性两方面研究危机情报可靠性对公共危机伪信息管理绩效的影响,并构建了混沌情景仿真模型,形成4个仿真方案;在此基础上利用所构建的仿真模型对4个仿真方案进行唯象仿真,最后得出相应研究结论. 相似文献
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【目的/意义】社交媒体在改变新闻传播以及人类获取信息方式的同时,也成为了虚假新闻传播的主要渠
道。因此,快速识别社交媒体中的虚假新闻,扼制虚假信息的传播,对净化网络空间、维护公共安全至关重要。【方
法/过程】为了有效识别社交媒体上发布的虚假新闻,本文基于对虚假新闻内容特征的深入剖析,分别设计了文本
词向量、文本情感、图像底层、图像语义特征的表示方法,用以提取社交网络中虚假新闻的图像特征信息和文本特
征信息,构建多模态特征融合的虚假新闻检测模型,并使用MediaEval2015数据集对模型性能进行效果验证。【结果/
结论】通过对比分析不同特征组合方式和不同分类方法的实验结果,发现融合文本特征和图像特征的多模态模型
可以有效提升虚假新闻检测效果。【创新/局限】研究从多模态的角度设计了虚假新闻检测模型,融合了文本与图像
的多种特征。然而采用向量拼接来实现特征融合,不仅无法实现各种特征的充分互补,而且容易造成维度灾难。 相似文献
13.
《Information processing & management》2022,59(1):102739
Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions. 相似文献
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《Information processing & management》2023,60(2):103206
A news article’s online audience provides useful insights about the article’s identity. However, fake news classifiers using such information risk relying on profiling. In response to the rising demand for ethical AI, we present a profiling-avoiding algorithm that leverages Twitter users during model optimisation while excluding them when an article’s veracity is evaluated. For this, we take inspiration from the social sciences and introduce two objective functions that maximise correlation between the article and its spreaders, and among those spreaders. We applied our profiling-avoiding algorithm to three popular neural classifiers and obtained results on fake news data discussing a variety of news topics. The positive impact on prediction performance demonstrates the soundness of the proposed objective functions to integrate social context in text-based classifiers. Moreover, statistical visualisation and dimension reduction techniques show that the user-inspired classifiers better discriminate between unseen fake and true news in their latent spaces. Our study serves as a stepping stone to resolve the underexplored issue of profiling-dependent decision-making in user-informed fake news detection. 相似文献
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《Information processing & management》2020,57(2):102025
Over the recent years, the growth of online social media has greatly facilitated the way people communicate with each other. Users of online social media share information, connect with other people and stay informed about trending events. However, much recent information appearing on social media is dubious and, in some cases, intended to mislead. Such content is often called fake news. Large amounts of online fake news has the potential to cause serious problems in society. Many point to the 2016 U.S. presidential election campaign as having been influenced by fake news. Subsequent to this election, the term has entered the mainstream vernacular. Moreover it has drawn the attention of industry and academia, seeking to understand its origins, distribution and effects.Of critical interest is the ability to detect when online content is untrue and intended to mislead. This is technically challenging for several reasons. Using social media tools, content is easily generated and quickly spread, leading to a large volume of content to analyse. Online information is very diverse, covering a large number of subjects, which contributes complexity to this task. The truth and intent of any statement often cannot be assessed by computers alone, so efforts must depend on collaboration between humans and technology. For instance, some content that is deemed by experts of being false and intended to mislead are available. While these sources are in limited supply, they can form a basis for such a shared effort.In this survey, we present a comprehensive overview of the finding to date relating to fake news. We characterize the negative impact of online fake news, and the state-of-the-art in detection methods. Many of these rely on identifying features of the users, content, and context that indicate misinformation. We also study existing datasets that have been used for classifying fake news. Finally, we propose promising research directions for online fake news analysis. 相似文献
17.
以淘宝为例,通过对43万条评论语料进行分析以及乔装淘宝店主获取的事实数据,从在线商品虚假评论实际解决需要出发,为在线商品虚假评论界定了新的含义,归纳了在线商品虚假评论的影响,最后全面分析了由在线评论者、在线销售商家、在线商品交易平台、虚假评论中介四大主体所组成体系中在线商品虚假评论的六大形成路径、形成动因及特点.本文对下一步的在线商品虚假评论识别技术等相关研究有极强的理论和实践指导意义. 相似文献
18.
《Information processing & management》2022,59(5):103063
In recent years, fake news detection has been a significant task attracting much attention. However, most current approaches utilize the features from a single modality, such as text or image, while the comprehensive fusion between features of different modalities has been ignored. To deal with the above problem, we propose a novel model named Bidirectional Cross-Modal Fusion (BCMF), which comprehensively integrates the textual and visual representations in a bidirectional manner. Specifically, the proposed model is decomposed into four submodules, i.e., the input embedding, the image2text fusion, the text2image fusion, and the prediction module. We conduct intensive experiments on four real-world datasets, i.e., Weibo, Twitter, Politi, and Gossip. The results show 2.2, 2.5, 4.9, and 3.1 percentage points of improvements in classification accuracy compared to the state-of-the-art methods on Weibo, Twitter, Politi, and Gossip, respectively. The experimental results suggest that the proposed model could better capture integrated information of different modalities and has high generalizability among different datasets. Further experiments suggest that the bidirectional fusions, the number of multi-attention heads, and the aggregating function could impact the performance of the cross-modal fake news detection. The research sheds light on the role of bidirectional cross-modal fusion in leveraging multi-modal information to improve the effect of fake news detection. 相似文献
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《Information processing & management》2022,59(1):102740
One of the most time-critical challenges for the Natural Language Processing (NLP) community is to combat the spread of fake news and misinformation. Existing approaches for misinformation detection use neural network models, statistical methods, linguistic traits, fact-checking strategies, etc. However, the menace of fake news seems to grow more vigorous with the advent of humongous and unusually creative language models. Relevant literature reveals that one major characteristic of the virality of fake news is the presence of an element of surprise in the story, which attracts immediate attention and invokes strong emotional stimulus in the reader. In this work, we leverage this idea and propose textual novelty detection and emotion prediction as the two tasks relating to automatic misinformation detection. We re-purpose textual entailment for novelty detection and use the models trained on large-scale datasets of entailment and emotion to classify fake information. Our results correlate with the idea as we achieve state-of-the-art (SOTA) performance (7.92%, 1.54%, 17.31% and 8.13% improvement in terms of accuracy) on four large-scale misinformation datasets. We hope that our current probe will motivate the community to explore further research on misinformation detection along this line. The source code is available at the GitHub.2 相似文献
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《Information processing & management》2023,60(1):103120
Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method. 相似文献